Overview

Dataset statistics

Number of variables26
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 KiB
Average record size in memory367.5 B

Variable types

Numeric18
Categorical3
Boolean5

Alerts

X_patient_id is highly overall correlated with X_compactness2 and 5 other fieldsHigh correlation
X_meshVolume is highly overall correlated with X_voxelVolume and 15 other fieldsHigh correlation
X_voxelVolume is highly overall correlated with X_meshVolume and 15 other fieldsHigh correlation
X_surfaceArea is highly overall correlated with X_meshVolume and 12 other fieldsHigh correlation
X_surfaceVolumeRatio is highly overall correlated with X_meshVolume and 16 other fieldsHigh correlation
X_sphericity is highly overall correlated with X_compactness1 and 10 other fieldsHigh correlation
X_compactness1 is highly overall correlated with X_sphericity and 10 other fieldsHigh correlation
X_compactness2 is highly overall correlated with X_patient_id and 10 other fieldsHigh correlation
X_sphericalDisproportion is highly overall correlated with X_meshVolume and 10 other fieldsHigh correlation
X_maximum3DDiameter is highly overall correlated with X_meshVolume and 17 other fieldsHigh correlation
X_maximum2DDiameterSlice is highly overall correlated with X_meshVolume and 11 other fieldsHigh correlation
X_maximum2DDiameterColumn is highly overall correlated with X_meshVolume and 22 other fieldsHigh correlation
X_maximum2DDiameterRow is highly overall correlated with X_patient_id and 17 other fieldsHigh correlation
X_majorAxisLength is highly overall correlated with X_meshVolume and 16 other fieldsHigh correlation
X_minorAxisLength is highly overall correlated with X_meshVolume and 17 other fieldsHigh correlation
X_leastAxisLength is highly overall correlated with X_meshVolume and 14 other fieldsHigh correlation
X_elongation is highly overall correlated with X_surfaceVolumeRatio and 9 other fieldsHigh correlation
X_flatness is highly overall correlated with X_patient_id and 12 other fieldsHigh correlation
Y_Functional_adenoma is highly overall correlated with X_meshVolume and 10 other fieldsHigh correlation
Y_Microadenoma is highly overall correlated with X_surfaceArea and 7 other fieldsHigh correlation
Y_Bleeding is highly overall correlated with X_patient_id and 4 other fieldsHigh correlation
Y_Cystic_part is highly overall correlated with X_patient_id and 9 other fieldsHigh correlation
Y_Chiasmal_compression is highly overall correlated with X_surfaceVolumeRatio and 6 other fieldsHigh correlation
Y_Hypopituitarism is highly overall correlated with X_patient_id and 3 other fieldsHigh correlation
Y_Signal is highly overall correlated with X_meshVolume and 13 other fieldsHigh correlation
Y_Pituitary_stalk_visible is highly overall correlated with X_meshVolume and 12 other fieldsHigh correlation
Y_Functional_adenoma is uniformly distributedUniform
X_patient_id has unique valuesUnique
X_meshVolume has unique valuesUnique
X_voxelVolume has unique valuesUnique
X_surfaceArea has unique valuesUnique
X_surfaceVolumeRatio has unique valuesUnique
X_sphericity has unique valuesUnique
X_compactness1 has unique valuesUnique
X_compactness2 has unique valuesUnique
X_sphericalDisproportion has unique valuesUnique
X_maximum3DDiameter has unique valuesUnique
X_majorAxisLength has unique valuesUnique
X_minorAxisLength has unique valuesUnique
X_leastAxisLength has unique valuesUnique
X_elongation has unique valuesUnique
X_flatness has unique valuesUnique

Reproduction

Analysis started2022-11-26 08:12:33.106160
Analysis finished2022-11-26 08:14:13.811377
Duration1 minute and 40.71 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

X_patient_id
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.863636
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:13.969205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median11.5
Q318.5
95-th percentile23.95
Maximum38
Range37
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation8.9511662
Coefficient of variation (CV)0.69585038
Kurtosis1.3968432
Mean12.863636
Median Absolute Deviation (MAD)6
Skewness1.0167568
Sum283
Variance80.123377
MonotonicityStrictly increasing
2022-11-26T09:14:14.219773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 1
 
4.5%
2 1
 
4.5%
24 1
 
4.5%
23 1
 
4.5%
22 1
 
4.5%
20 1
 
4.5%
19 1
 
4.5%
17 1
 
4.5%
15 1
 
4.5%
14 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
6 1
4.5%
7 1
4.5%
8 1
4.5%
9 1
4.5%
10 1
4.5%
ValueCountFrequency (%)
38 1
4.5%
24 1
4.5%
23 1
4.5%
22 1
4.5%
20 1
4.5%
19 1
4.5%
17 1
4.5%
15 1
4.5%
14 1
4.5%
13 1
4.5%

X_meshVolume
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6092.8144
Minimum780.08333
Maximum23308.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:14.491325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum780.08333
5-th percentile1248.3771
Q11742.5208
median3179.4583
Q39580.0417
95-th percentile14065.281
Maximum23308.5
Range22528.417
Interquartile range (IQR)7837.5208

Descriptive statistics

Standard deviation5746.8024
Coefficient of variation (CV)0.94320983
Kurtosis2.3249653
Mean6092.8144
Median Absolute Deviation (MAD)1917.5625
Skewness1.4804735
Sum134041.92
Variance33025738
MonotonicityNot monotonic
2022-11-26T09:14:14.710586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3694.458333 1
 
4.5%
7684.333333 1
 
4.5%
9780.708333 1
 
4.5%
2710.666667 1
 
4.5%
1824.958333 1
 
4.5%
1276.916667 1
 
4.5%
11272.58333 1
 
4.5%
8978.041667 1
 
4.5%
2721.916667 1
 
4.5%
14112.29167 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
780.0833333 1
4.5%
1246.875 1
4.5%
1276.916667 1
4.5%
1350.333333 1
4.5%
1706.916667 1
4.5%
1715.041667 1
4.5%
1824.958333 1
4.5%
2710.666667 1
4.5%
2721.916667 1
4.5%
2725.708333 1
4.5%
ValueCountFrequency (%)
23308.5 1
4.5%
14112.29167 1
4.5%
13172.08333 1
4.5%
11272.58333 1
4.5%
10453.375 1
4.5%
9780.708333 1
4.5%
8978.041667 1
4.5%
7684.333333 1
4.5%
7167.208333 1
4.5%
3694.458333 1
4.5%

X_voxelVolume
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6118.2727
Minimum793
Maximum23370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:14.942394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum793
5-th percentile1266.3
Q11758.5
median3210
Q39610.25
95-th percentile14101.8
Maximum23370
Range22577
Interquartile range (IQR)7851.75

Descriptive statistics

Standard deviation5756.6864
Coefficient of variation (CV)0.94090059
Kurtosis2.3318979
Mean6118.2727
Median Absolute Deviation (MAD)1932
Skewness1.4814608
Sum134602
Variance33139438
MonotonicityNot monotonic
2022-11-26T09:14:15.180756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3716 1
 
4.5%
7709 1
 
4.5%
9811 1
 
4.5%
2741 1
 
4.5%
1841 1
 
4.5%
1291 1
 
4.5%
11303 1
 
4.5%
9008 1
 
4.5%
2740 1
 
4.5%
14149 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
793 1
4.5%
1265 1
4.5%
1291 1
4.5%
1365 1
4.5%
1723 1
4.5%
1731 1
4.5%
1841 1
4.5%
2740 1
4.5%
2741 1
4.5%
2744 1
4.5%
ValueCountFrequency (%)
23370 1
4.5%
14149 1
4.5%
13205 1
4.5%
11303 1
4.5%
10483 1
4.5%
9811 1
4.5%
9008 1
4.5%
7709 1
4.5%
7194 1
4.5%
3716 1
4.5%

X_surfaceArea
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1739.2572
Minimum503.76663
Maximum4870.5588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:15.438576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum503.76663
5-th percentile645.65707
Q1821.00371
median1238.8118
Q32530.7788
95-th percentile3470.7688
Maximum4870.5588
Range4366.7922
Interquartile range (IQR)1709.7751

Descriptive statistics

Standard deviation1150.9277
Coefficient of variation (CV)0.66173517
Kurtosis0.90607465
Mean1739.2572
Median Absolute Deviation (MAD)577.60579
Skewness1.1316539
Sum38263.658
Variance1324634.5
MonotonicityNot monotonic
2022-11-26T09:14:15.692895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1353.87366 1
 
4.5%
2103.195491 1
 
4.5%
2538.91508 1
 
4.5%
1069.592821 1
 
4.5%
820.5404286 1
 
4.5%
643.9294109 1
 
4.5%
2771.43205 1
 
4.5%
2506.369852 1
 
4.5%
1069.537584 1
 
4.5%
3486.569981 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
503.7666307 1
4.5%
643.9294109 1
4.5%
678.4826505 1
4.5%
768.5426154 1
4.5%
772.1953409 1
4.5%
820.5404286 1
4.5%
822.3935577 1
4.5%
1048.235024 1
4.5%
1069.537584 1
4.5%
1069.592821 1
4.5%
ValueCountFrequency (%)
4870.558816 1
4.5%
3486.569981 1
4.5%
3170.547167 1
4.5%
2771.43205 1
4.5%
2685.113413 1
4.5%
2538.91508 1
4.5%
2506.369852 1
4.5%
2103.195491 1
4.5%
2098.518793 1
4.5%
1357.59755 1
4.5%

X_surfaceVolumeRatio
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3763399
Minimum0.20896063
Maximum0.64578566
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:15.952481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.20896063
5-th percentile0.24095973
Q10.26311276
median0.37941895
Q30.45009192
95-th percentile0.61077051
Maximum0.64578566
Range0.43682503
Interquartile range (IQR)0.18697915

Descriptive statistics

Standard deviation0.12372615
Coefficient of variation (CV)0.32876171
Kurtosis-0.33558816
Mean0.3763399
Median Absolute Deviation (MAD)0.10405075
Skewness0.61036361
Sum8.2794778
Variance0.01530816
MonotonicityNot monotonic
2022-11-26T09:14:16.198810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.3664606656 1
 
4.5%
0.2736991487 1
 
4.5%
0.2595839681 1
 
4.5%
0.3945866283 1
 
4.5%
0.4496214591 1
 
4.5%
0.5042846003 1
 
4.5%
0.2458559824 1
 
4.5%
0.2791666541 1
 
4.5%
0.3929354624 1
 
4.5%
0.2470590931 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.2089606288 1
4.5%
0.2407020277 1
4.5%
0.2458559824 1
4.5%
0.2470590931 1
4.5%
0.2568656929 1
4.5%
0.2595839681 1
4.5%
0.2736991487 1
4.5%
0.2791666541 1
4.5%
0.2927944459 1
4.5%
0.3664606656 1
4.5%
ValueCountFrequency (%)
0.6457856606 1
4.5%
0.6163750299 1
4.5%
0.5042846003 1
4.5%
0.502455678 1
4.5%
0.481800649 1
4.5%
0.450248735 1
4.5%
0.4496214591 1
4.5%
0.4113977103 1
4.5%
0.3945866283 1
4.5%
0.3929354624 1
4.5%

X_sphericity
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85347661
Minimum0.72895063
Maximum0.90021251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:16.426963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.72895063
5-th percentile0.81000738
Q10.8401325
median0.85884867
Q30.8798536
95-th percentile0.89720883
Maximum0.90021251
Range0.17126189
Interquartile range (IQR)0.039721101

Descriptive statistics

Standard deviation0.039009462
Coefficient of variation (CV)0.045706539
Kurtosis3.7693909
Mean0.85347661
Median Absolute Deviation (MAD)0.020716468
Skewness-1.5905915
Sum18.776485
Variance0.0015217381
MonotonicityNot monotonic
2022-11-26T09:14:16.664688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.8536368753 1
 
4.5%
0.8953825223 1
 
4.5%
0.8711297015 1
 
4.5%
0.8789882214 1
 
4.5%
0.8801420614 1
 
4.5%
0.8839361592 1
 
4.5%
0.8772610739 1
 
4.5%
0.8334765349 1
 
4.5%
0.8814640893 1
 
4.5%
0.8099982868 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.7289506296 1
4.5%
0.8099982868 1
4.5%
0.8101800586 1
4.5%
0.8134834603 1
4.5%
0.8334765349 1
4.5%
0.8398711002 1
4.5%
0.8409167008 1
4.5%
0.8409587933 1
4.5%
0.8507191697 1
4.5%
0.8536368753 1
4.5%
ValueCountFrequency (%)
0.9002125148 1
4.5%
0.8973049522 1
4.5%
0.8953825223 1
4.5%
0.8839361592 1
4.5%
0.8814640893 1
4.5%
0.8801420614 1
4.5%
0.8789882214 1
4.5%
0.8772610739 1
4.5%
0.8711297015 1
4.5%
0.8707751699 1
4.5%

X_compactness1
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.041861499
Minimum0.033017642
Maximum0.045312335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:16.903888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.033017642
5-th percentile0.038675179
Q10.040852685
median0.042225511
Q30.043783911
95-th percentile0.045085742
Maximum0.045312335
Range0.012294694
Interquartile range (IQR)0.0029312264

Descriptive statistics

Standard deviation0.0028195069
Coefficient of variation (CV)0.067353224
Kurtosis3.3694203
Mean0.041861499
Median Absolute Deviation (MAD)0.0015368716
Skewness-1.4971481
Sum0.92095298
Variance7.9496193 × 10-6
MonotonicityNot monotonic
2022-11-26T09:14:17.155868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.04184163321 1
 
4.5%
0.04494814737 1
 
4.5%
0.04313433521 1
 
4.5%
0.04371932622 1
 
4.5%
0.04380543941 1
 
4.5%
0.0440889978 1
 
4.5%
0.04359053162 1
 
4.5%
0.04036815895 1
 
4.5%
0.0439041742 1
 
4.5%
0.03867452847 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.03301764168 1
4.5%
0.03867452847 1
4.5%
0.03868754764 1
4.5%
0.03892440367 1
4.5%
0.04036815895 1
4.5%
0.04083361533 1
4.5%
0.04090989299 1
4.5%
0.04091296468 1
4.5%
0.04162729642 1
4.5%
0.04184163321 1
4.5%
ValueCountFrequency (%)
0.04531233539 1
4.5%
0.04509298383 1
4.5%
0.04494814737 1
4.5%
0.0440889978 1
4.5%
0.0439041742 1
4.5%
0.04380543941 1
4.5%
0.04371932622 1
4.5%
0.04359053162 1
4.5%
0.04313433521 1
4.5%
0.04310800573 1
4.5%

X_compactness2
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62532868
Minimum0.38734178
Maximum0.72951653
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:17.419164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.38734178
5-th percentile0.53145552
Q10.59298504
median0.63351728
Q30.6811326
95-th percentile0.72223894
Maximum0.72951653
Range0.34217475
Interquartile range (IQR)0.088147564

Descriptive statistics

Standard deviation0.08014474
Coefficient of variation (CV)0.12816418
Kurtosis2.3059255
Mean0.62532868
Median Absolute Deviation (MAD)0.04694583
Skewness-1.2309965
Sum13.757231
Variance0.0064231794
MonotonicityNot monotonic
2022-11-26T09:14:17.686761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.6220417039 1
 
4.5%
0.7178369976 1
 
4.5%
0.6610715457 1
 
4.5%
0.6791241376 1
 
4.5%
0.6818020903 1
 
4.5%
0.6906574484 1
 
4.5%
0.6751287109 1
 
4.5%
0.5790020916 1
 
4.5%
0.6848790345 1
 
4.5%
0.5314376278 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.387341782 1
4.5%
0.5314376278 1
4.5%
0.5317954882 1
4.5%
0.5383270239 1
4.5%
0.5790020916 1
4.5%
0.5924311868 1
4.5%
0.5946465906 1
4.5%
0.5947358911 1
4.5%
0.6156851196 1
4.5%
0.6220417039 1
4.5%
ValueCountFrequency (%)
0.7295165328 1
4.5%
0.722470625 1
4.5%
0.7178369976 1
4.5%
0.6906574484 1
4.5%
0.6848790345 1
4.5%
0.6818020903 1
4.5%
0.6791241376 1
4.5%
0.6751287109 1
4.5%
0.6610715457 1
4.5%
0.6602647471 1
4.5%

X_sphericalDisproportion
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1741999
Minimum1.1108488
Maximum1.371835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:17.953856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.1108488
5-th percentile1.114568
Q11.1365531
median1.1643571
Q31.1902888
95-th percentile1.2345567
Maximum1.371835
Range0.26098621
Interquartile range (IQR)0.053735699

Descriptive statistics

Standard deviation0.057959588
Coefficient of variation (CV)0.049360923
Kurtosis5.584654
Mean1.1741999
Median Absolute Deviation (MAD)0.027431141
Skewness1.9832662
Sum25.832397
Variance0.0033593139
MonotonicityNot monotonic
2022-11-26T09:14:18.209620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1.171458297 1
 
4.5%
1.116841099 1
 
4.5%
1.147934686 1
 
4.5%
1.137671673 1
 
4.5%
1.136180219 1
 
4.5%
1.13130342 1
 
4.5%
1.139911515 1
 
4.5%
1.199793825 1
 
4.5%
1.134476165 1
 
4.5%
1.234570512 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1.110848809 1
4.5%
1.114448324 1
4.5%
1.116841099 1
4.5%
1.13130342 1
4.5%
1.134476165 1
4.5%
1.136180219 1
4.5%
1.137671673 1
4.5%
1.139911515 1
4.5%
1.147934686 1
4.5%
1.148402061 1
4.5%
ValueCountFrequency (%)
1.371835018 1
4.5%
1.234570512 1
4.5%
1.234293524 1
4.5%
1.229281293 1
4.5%
1.199793825 1
4.5%
1.1906589 1
4.5%
1.189178428 1
4.5%
1.189118906 1
4.5%
1.175476039 1
4.5%
1.171458297 1
4.5%

X_maximum3DDiameter
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.126265
Minimum16.124515
Maximum52.153619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:18.590282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16.124515
5-th percentile16.656728
Q119.446691
median24.22636
Q333.073546
95-th percentile46.628448
Maximum52.153619
Range36.029104
Interquartile range (IQR)13.626855

Descriptive statistics

Standard deviation9.8334853
Coefficient of variation (CV)0.36250789
Kurtosis0.80058045
Mean27.126265
Median Absolute Deviation (MAD)6.2120156
Skewness1.0966319
Sum596.77784
Variance96.697433
MonotonicityNot monotonic
2022-11-26T09:14:18.840474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
24.51530134 1
 
4.5%
29.68164416 1
 
4.5%
30.69201851 1
 
4.5%
21.44761059 1
 
4.5%
18.70828693 1
 
4.5%
16.64331698 1
 
4.5%
33.86738844 1
 
4.5%
34.525353 1
 
4.5%
21.40093456 1
 
4.5%
47.10626285 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
16.1245155 1
4.5%
16.64331698 1
4.5%
16.91153453 1
4.5%
18.05547009 1
4.5%
18.70828693 1
4.5%
19.28730152 1
4.5%
19.92485885 1
4.5%
21.40093456 1
4.5%
21.44761059 1
4.5%
23.85372088 1
4.5%
ValueCountFrequency (%)
52.15361924 1
4.5%
47.10626285 1
4.5%
37.54996671 1
4.5%
35.21363372 1
4.5%
34.525353 1
4.5%
33.86738844 1
4.5%
30.69201851 1
4.5%
30.47950131 1
4.5%
29.68164416 1
4.5%
24.69817807 1
4.5%

X_maximum2DDiameterSlice
Real number (ℝ)

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.812503
Minimum15.811388
Maximum44.654227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:19.165333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum15.811388
5-th percentile15.82238
Q118.546307
median22.90872
Q328.328933
95-th percentile30.842546
Maximum44.654227
Range28.842839
Interquartile range (IQR)9.7826255

Descriptive statistics

Standard deviation6.8910691
Coefficient of variation (CV)0.28938869
Kurtosis2.500023
Mean23.812503
Median Absolute Deviation (MAD)5.0246919
Skewness1.2388259
Sum523.87506
Variance47.486834
MonotonicityNot monotonic
2022-11-26T09:14:19.484934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
15.8113883 2
 
9.1%
23.34523506 1
 
4.5%
26.41968963 1
 
4.5%
30 1
 
4.5%
20.61552813 1
 
4.5%
17.4642492 1
 
4.5%
29.68164416 1
 
4.5%
29.15475947 1
 
4.5%
18.43908891 1
 
4.5%
27.51363298 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
15.8113883 2
9.1%
16.03121954 1
4.5%
17.20465053 1
4.5%
17.4642492 1
4.5%
18.43908891 1
4.5%
18.86796226 1
4.5%
19.6977156 1
4.5%
20.61552813 1
4.5%
20.88061302 1
4.5%
22.47220505 1
4.5%
ValueCountFrequency (%)
44.65422712 1
4.5%
30.88689042 1
4.5%
30 1
4.5%
29.68164416 1
4.5%
29.15475947 1
4.5%
28.60069929 1
4.5%
27.51363298 1
4.5%
26.41968963 1
4.5%
25.8069758 1
4.5%
24.51530134 1
4.5%

X_maximum2DDiameterColumn
Real number (ℝ)

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.64861
Minimum16.124515
Maximum42.755117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:19.809068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16.124515
5-th percentile16.179886
Q119.002941
median23.437725
Q331.749419
95-th percentile39.466893
Maximum42.755117
Range26.630601
Interquartile range (IQR)12.746478

Descriptive statistics

Standard deviation8.1861482
Coefficient of variation (CV)0.31916537
Kurtosis-0.81673476
Mean25.64861
Median Absolute Deviation (MAD)6.3951429
Skewness0.59941445
Sum564.26942
Variance67.013023
MonotonicityNot monotonic
2022-11-26T09:14:20.104106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
29.83286778 2
 
9.1%
16.64331698 2
 
9.1%
20.24845673 1
 
4.5%
20.59126028 1
 
4.5%
18.60107524 1
 
4.5%
16.15549442 1
 
4.5%
32.38826948 1
 
4.5%
33.61547263 1
 
4.5%
21.02379604 1
 
4.5%
42.75511665 1
 
4.5%
Other values (10) 10
45.5%
ValueCountFrequency (%)
16.1245155 1
4.5%
16.15549442 1
4.5%
16.64331698 2
9.1%
18.60107524 1
4.5%
18.78829423 1
4.5%
19.6468827 1
4.5%
20.24845673 1
4.5%
20.59126028 1
4.5%
21.02379604 1
4.5%
23.02172887 1
4.5%
ValueCountFrequency (%)
42.75511665 1
4.5%
39.62322551 1
4.5%
36.49657518 1
4.5%
34.71310992 1
4.5%
33.61547263 1
4.5%
32.38826948 1
4.5%
29.83286778 2
9.1%
29.15475947 1
4.5%
24.51530134 1
4.5%
23.85372088 1
4.5%

X_maximum2DDiameterRow
Real number (ℝ)

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.041819
Minimum10.816654
Maximum46.572524
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:20.486680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10.816654
5-th percentile13.385214
Q116.049305
median20.836944
Q330.363762
95-th percentile45.941189
Maximum46.572524
Range35.75587
Interquartile range (IQR)14.314457

Descriptive statistics

Standard deviation10.322885
Coefficient of variation (CV)0.42937204
Kurtosis0.050627145
Mean24.041819
Median Absolute Deviation (MAD)6.5410089
Skewness0.89635346
Sum528.92001
Variance106.56195
MonotonicityNot monotonic
2022-11-26T09:14:20.743430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
14.56021978 2
 
9.1%
22.36067977 1
 
4.5%
18.78829423 1
 
4.5%
29.42787794 1
 
4.5%
17.49285568 1
 
4.5%
16.76305461 1
 
4.5%
13.34166406 1
 
4.5%
33.54101966 1
 
4.5%
30.6757233 1
 
4.5%
19.31320792 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
10.81665383 1
4.5%
13.34166406 1
4.5%
14.2126704 1
4.5%
14.56021978 2
9.1%
15.8113883 1
4.5%
16.76305461 1
4.5%
17.49285568 1
4.5%
18.38477631 1
4.5%
18.78829423 1
4.5%
19.31320792 1
4.5%
ValueCountFrequency (%)
46.57252409 1
4.5%
46.40043103 1
4.5%
37.21558813 1
4.5%
33.54101966 1
4.5%
31.40063694 1
4.5%
30.6757233 1
4.5%
29.42787794 1
4.5%
27.51363298 1
4.5%
27.29468813 1
4.5%
22.47220505 1
4.5%

X_majorAxisLength
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.208193
Minimum14.66905
Maximum43.73276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:21.018942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum14.66905
5-th percentile14.871056
Q116.57663
median20.098031
Q326.631849
95-th percentile39.607445
Maximum43.73276
Range29.06371
Interquartile range (IQR)10.055219

Descriptive statistics

Standard deviation8.1390334
Coefficient of variation (CV)0.35069656
Kurtosis0.77889909
Mean23.208193
Median Absolute Deviation (MAD)5.2138776
Skewness1.0964367
Sum510.58025
Variance66.243865
MonotonicityNot monotonic
2022-11-26T09:14:21.236703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
20.82409114 1
 
4.5%
24.62179946 1
 
4.5%
26.12091132 1
 
4.5%
18.84529593 1
 
4.5%
15.85333477 1
 
4.5%
14.66905026 1
 
4.5%
26.89485911 1
 
4.5%
26.48479514 1
 
4.5%
17.35071876 1
 
4.5%
40.00486494 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
14.66905026 1
4.5%
14.86960037 1
4.5%
14.89870722 1
4.5%
15.18838431 1
4.5%
15.85333477 1
4.5%
16.31860051 1
4.5%
17.35071876 1
4.5%
18.76987174 1
4.5%
18.84529593 1
4.5%
19.28814338 1
4.5%
ValueCountFrequency (%)
43.73276028 1
4.5%
40.00486494 1
4.5%
32.05646892 1
4.5%
31.35064883 1
4.5%
26.89485911 1
4.5%
26.68086747 1
4.5%
26.48479514 1
4.5%
26.38450392 1
4.5%
26.12091132 1
4.5%
24.62179946 1
4.5%

X_minorAxisLength
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.3305
Minimum9.7897163
Maximum30.487811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:21.488291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum9.7897163
5-th percentile10.695186
Q113.488833
median17.579444
Q323.228595
95-th percentile25.258392
Maximum30.487811
Range20.698094
Interquartile range (IQR)9.7397617

Descriptive statistics

Standard deviation5.7696482
Coefficient of variation (CV)0.31475674
Kurtosis-0.91157912
Mean18.3305
Median Absolute Deviation (MAD)4.6001378
Skewness0.29500034
Sum403.27099
Variance33.288841
MonotonicityNot monotonic
2022-11-26T09:14:21.719042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
18.98469563 1
 
4.5%
21.38845951 1
 
4.5%
24.68282685 1
 
4.5%
15.3745194 1
 
4.5%
13.92008386 1
 
4.5%
11.48549719 1
 
4.5%
25.25958774 1
 
4.5%
23.93960135 1
 
4.5%
15.29579853 1
 
4.5%
23.60606168 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
9.789716335 1
4.5%
10.65359075 1
4.5%
11.48549719 1
4.5%
12.05292855 1
4.5%
12.89591878 1
4.5%
13.34508289 1
4.5%
13.92008386 1
4.5%
15.29579853 1
4.5%
15.3745194 1
4.5%
15.61578855 1
4.5%
ValueCountFrequency (%)
30.48781083 1
4.5%
25.25958774 1
4.5%
25.23567485 1
4.5%
24.68282685 1
4.5%
23.93960135 1
4.5%
23.60606168 1
4.5%
22.09619443 1
4.5%
22.00226413 1
4.5%
21.38845951 1
4.5%
18.98469563 1
4.5%

X_leastAxisLength
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.966906
Minimum8.0801254
Maximum25.801313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:21.939463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum8.0801254
5-th percentile8.2748093
Q111.678398
median14.717517
Q320.971026
95-th percentile23.950571
Maximum25.801313
Range17.721187
Interquartile range (IQR)9.2926273

Descriptive statistics

Standard deviation5.4457471
Coefficient of variation (CV)0.34106464
Kurtosis-1.2688124
Mean15.966906
Median Absolute Deviation (MAD)4.1709573
Skewness0.27221784
Sum351.27194
Variance29.656162
MonotonicityNot monotonic
2022-11-26T09:14:22.157788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
13.69698294 1
 
4.5%
20.25777174 1
 
4.5%
21.37028024 1
 
4.5%
13.25528271 1
 
4.5%
11.8468342 1
 
4.5%
10.77773284 1
 
4.5%
23.11150634 1
 
4.5%
21.01299486 1
 
4.5%
14.52843147 1
 
4.5%
21.78528425 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
8.080125416 1
4.5%
8.167410451 1
4.5%
10.31538691 1
4.5%
10.77773284 1
4.5%
10.87446617 1
4.5%
11.64424253 1
4.5%
11.78086548 1
4.5%
11.8468342 1
4.5%
13.25528271 1
4.5%
13.69698294 1
4.5%
ValueCountFrequency (%)
25.80131255 1
4.5%
23.99473182 1
4.5%
23.11150634 1
4.5%
21.78528425 1
4.5%
21.37028024 1
4.5%
21.01299486 1
4.5%
20.84511785 1
4.5%
20.25777174 1
4.5%
17.68398482 1
4.5%
15.53458895 1
4.5%

X_elongation
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.80683553
Minimum0.39929702
Maximum0.95693185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:22.382791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.39929702
5-th percentile0.59349434
Q10.71859793
median0.85129418
Q30.90261401
95-th percentile0.94465771
Maximum0.95693185
Range0.55763483
Interquartile range (IQR)0.18401608

Descriptive statistics

Standard deviation0.13752321
Coefficient of variation (CV)0.17044764
Kurtosis2.2874472
Mean0.80683553
Median Absolute Deviation (MAD)0.064347498
Skewness-1.4456082
Sum17.750382
Variance0.018912634
MonotonicityNot monotonic
2022-11-26T09:14:22.593615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.9116698302 1
 
4.5%
0.8686797872 1
 
4.5%
0.9449450882 1
 
4.5%
0.8158279634 1
 
4.5%
0.8780539905 1
 
4.5%
0.782974834 1
 
4.5%
0.9391976227 1
 
4.5%
0.903899812 1
 
4.5%
0.8815656999 1
 
4.5%
0.5900797743 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.3992970155 1
4.5%
0.5900797743 1
4.5%
0.6583711796 1
4.5%
0.687054177 1
4.5%
0.6892897181 1
4.5%
0.6971389556 1
4.5%
0.782974834 1
4.5%
0.8049490453 1
4.5%
0.8089915704 1
4.5%
0.8158279634 1
4.5%
ValueCountFrequency (%)
0.9569318487 1
4.5%
0.9449450882 1
4.5%
0.9391976227 1
4.5%
0.920161184 1
4.5%
0.9116698302 1
4.5%
0.903899812 1
4.5%
0.8987565964 1
4.5%
0.8815656999 1
4.5%
0.8786374257 1
4.5%
0.8780539905 1
4.5%

X_flatness
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70098506
Minimum0.30284343
Maximum0.91347312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.0 B
2022-11-26T09:14:22.812380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.30284343
5-th percentile0.54480103
Q10.61338095
median0.73230964
Q30.80239643
95-th percentile0.85822857
Maximum0.91347312
Range0.61062969
Interquartile range (IQR)0.18901548

Descriptive statistics

Standard deviation0.13926152
Coefficient of variation (CV)0.19866546
Kurtosis1.6960248
Mean0.70098506
Median Absolute Deviation (MAD)0.083933377
Skewness-1.0516332
Sum15.421671
Variance0.019393771
MonotonicityNot monotonic
2022-11-26T09:14:23.017831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.6577469746 1
 
4.5%
0.822757564 1
 
4.5%
0.8181291985 1
 
4.5%
0.7033735502 1
 
4.5%
0.7472771105 1
 
4.5%
0.7347260153 1
 
4.5%
0.8593280316 1
 
4.5%
0.7933984289 1
 
4.5%
0.8373388833 1
 
4.5%
0.5445658743 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.3028434299 1
4.5%
0.5445658743 1
4.5%
0.549268995 1
4.5%
0.5495715184 1
4.5%
0.5899767675 1
4.5%
0.6010871156 1
4.5%
0.6502624448 1
4.5%
0.6577469746 1
4.5%
0.6702413231 1
4.5%
0.7033735502 1
4.5%
ValueCountFrequency (%)
0.9134731219 1
4.5%
0.8593280316 1
4.5%
0.8373388833 1
4.5%
0.822757564 1
4.5%
0.8181291985 1
4.5%
0.8053957626 1
4.5%
0.7933984289 1
4.5%
0.7756496834 1
4.5%
0.7653663548 1
4.5%
0.7472771105 1
4.5%

Y_Functional_adenoma
Categorical

HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Nonfunctioning adenoma
11 
Functional adenoma
11 

Length

Max length22
Median length20
Mean length20
Min length18

Characters and Unicode

Total characters440
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNonfunctioning adenoma
2nd rowNonfunctioning adenoma
3rd rowFunctional adenoma
4th rowNonfunctioning adenoma
5th rowFunctional adenoma

Common Values

ValueCountFrequency (%)
Nonfunctioning adenoma 11
50.0%
Functional adenoma 11
50.0%

Length

2022-11-26T09:14:23.270155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-26T09:14:23.567637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
adenoma 22
50.0%
nonfunctioning 11
25.0%
functional 11
25.0%

Most occurring characters

ValueCountFrequency (%)
n 88
20.0%
o 55
12.5%
a 55
12.5%
i 33
 
7.5%
u 22
 
5.0%
c 22
 
5.0%
t 22
 
5.0%
22
 
5.0%
d 22
 
5.0%
e 22
 
5.0%
Other values (6) 77
17.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 396
90.0%
Space Separator 22
 
5.0%
Uppercase Letter 22
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 88
22.2%
o 55
13.9%
a 55
13.9%
i 33
 
8.3%
u 22
 
5.6%
c 22
 
5.6%
t 22
 
5.6%
d 22
 
5.6%
e 22
 
5.6%
m 22
 
5.6%
Other values (3) 33
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
N 11
50.0%
F 11
50.0%
Space Separator
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 418
95.0%
Common 22
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 88
21.1%
o 55
13.2%
a 55
13.2%
i 33
 
7.9%
u 22
 
5.3%
c 22
 
5.3%
t 22
 
5.3%
d 22
 
5.3%
e 22
 
5.3%
m 22
 
5.3%
Other values (5) 55
13.2%
Common
ValueCountFrequency (%)
22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 88
20.0%
o 55
12.5%
a 55
12.5%
i 33
 
7.5%
u 22
 
5.0%
c 22
 
5.0%
t 22
 
5.0%
22
 
5.0%
d 22
 
5.0%
e 22
 
5.0%
Other values (6) 77
17.5%

Y_Microadenoma
Categorical

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Macroadenoma
17 
Microadenoma

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters264
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMacroadenoma
2nd rowMacroadenoma
3rd rowMicroadenoma
4th rowMacroadenoma
5th rowMicroadenoma

Common Values

ValueCountFrequency (%)
Macroadenoma 17
77.3%
Microadenoma 5
 
22.7%

Length

2022-11-26T09:14:23.755839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-26T09:14:23.972823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
macroadenoma 17
77.3%
microadenoma 5
 
22.7%

Most occurring characters

ValueCountFrequency (%)
a 61
23.1%
o 44
16.7%
M 22
 
8.3%
c 22
 
8.3%
r 22
 
8.3%
d 22
 
8.3%
e 22
 
8.3%
n 22
 
8.3%
m 22
 
8.3%
i 5
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 242
91.7%
Uppercase Letter 22
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 61
25.2%
o 44
18.2%
c 22
 
9.1%
r 22
 
9.1%
d 22
 
9.1%
e 22
 
9.1%
n 22
 
9.1%
m 22
 
9.1%
i 5
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
M 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 264
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 61
23.1%
o 44
16.7%
M 22
 
8.3%
c 22
 
8.3%
r 22
 
8.3%
d 22
 
8.3%
e 22
 
8.3%
n 22
 
8.3%
m 22
 
8.3%
i 5
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 61
23.1%
o 44
16.7%
M 22
 
8.3%
c 22
 
8.3%
r 22
 
8.3%
d 22
 
8.3%
e 22
 
8.3%
n 22
 
8.3%
m 22
 
8.3%
i 5
 
1.9%

Y_Bleeding
Boolean

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size150.0 B
False
20 
True
 
2
ValueCountFrequency (%)
False 20
90.9%
True 2
 
9.1%
2022-11-26T09:14:24.203218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size150.0 B
False
14 
True
ValueCountFrequency (%)
False 14
63.6%
True 8
36.4%
2022-11-26T09:14:24.429970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size150.0 B
True
13 
False
ValueCountFrequency (%)
True 13
59.1%
False 9
40.9%
2022-11-26T09:14:24.654983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size150.0 B
False
15 
True
ValueCountFrequency (%)
False 15
68.2%
True 7
31.8%
2022-11-26T09:14:24.867959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Y_Signal
Categorical

Distinct3
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Hyposignal
12 
Hypersignal
Mixed

Length

Max length11
Median length10
Mean length9.6363636
Min length5

Characters and Unicode

Total characters212
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMixed
2nd rowHyposignal
3rd rowHyposignal
4th rowHypersignal
5th rowHyposignal

Common Values

ValueCountFrequency (%)
Hyposignal 12
54.5%
Hypersignal 7
31.8%
Mixed 3
 
13.6%

Length

2022-11-26T09:14:25.095286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-26T09:14:25.398503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
hyposignal 12
54.5%
hypersignal 7
31.8%
mixed 3
 
13.6%

Most occurring characters

ValueCountFrequency (%)
i 22
10.4%
H 19
9.0%
y 19
9.0%
p 19
9.0%
s 19
9.0%
g 19
9.0%
n 19
9.0%
a 19
9.0%
l 19
9.0%
o 12
5.7%
Other values (5) 26
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 190
89.6%
Uppercase Letter 22
 
10.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 22
11.6%
y 19
10.0%
p 19
10.0%
s 19
10.0%
g 19
10.0%
n 19
10.0%
a 19
10.0%
l 19
10.0%
o 12
6.3%
e 10
5.3%
Other values (3) 13
6.8%
Uppercase Letter
ValueCountFrequency (%)
H 19
86.4%
M 3
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 212
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 22
10.4%
H 19
9.0%
y 19
9.0%
p 19
9.0%
s 19
9.0%
g 19
9.0%
n 19
9.0%
a 19
9.0%
l 19
9.0%
o 12
5.7%
Other values (5) 26
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 22
10.4%
H 19
9.0%
y 19
9.0%
p 19
9.0%
s 19
9.0%
g 19
9.0%
n 19
9.0%
a 19
9.0%
l 19
9.0%
o 12
5.7%
Other values (5) 26
12.3%
Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size150.0 B
False
12 
True
10 
ValueCountFrequency (%)
False 12
54.5%
True 10
45.5%
2022-11-26T09:14:25.679736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Interactions

2022-11-26T09:14:07.136585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:02.903848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:07.318143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:10.847373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:14.297083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:17.958199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:21.562720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:25.379855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:29.147240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:32.781731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:36.496377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:40.341568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:43.966148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:47.698442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:51.606646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:55.274028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:58.808756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:02.810902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:07.440405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:03.212327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:07.522569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:11.050812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:14.509676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:18.170714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:21.780400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:25.626198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:29.364388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:32.983935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:36.709354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:40.553258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:44.177017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:47.917254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:51.834077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:55.499424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:59.027699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:03.021881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:07.675776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:03.463305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:07.692526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:11.225461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:14.680149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:18.348481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:21.975132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:25.832645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:29.550124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:33.166147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:36.897796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:40.741781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:44.355010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:48.140492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:52.022223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:55.697897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:59.332416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:03.200516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:07.899202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:03.714632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:07.862031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:11.399049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:14.861904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:18.524786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:22.170981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:26.038139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:29.733634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:33.346454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:37.094612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:40.924974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:44.537697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:48.329862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:52.204433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:55.905339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:59.522204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:03.382910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:08.116596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:03.967955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:08.041489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:11.575344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:15.051398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:18.709266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:22.375368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:26.241307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:29.917752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:33.534216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:37.283198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:41.114468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:44.720170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:48.518361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:52.399092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:56.100817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:59.711815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:03.575260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:08.367438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:04.212327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:08.213083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:11.759258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:15.249867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:18.879950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:22.573222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:26.428879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:30.102851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:33.714077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:37.478738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:41.301006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:44.906646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:48.745626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:52.587027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:56.287952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:59.910851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:03.757404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:08.656677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:04.511502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:08.417890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:11.971277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:15.483243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:19.093560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:22.797061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:26.640834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:30.317608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:33.931508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:37.695141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:41.520328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:45.148998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:48.970028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:52.801901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:56.492333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:00.142572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:03.982612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:08.916525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:04.775322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:08.612382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:12.152585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:15.702949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:19.282343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:23.007284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:26.830842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:30.515366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:34.130378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:37.895605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:41.718791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:45.366418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:49.292249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:52.999574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:56.682842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:00.353346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:04.207052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:09.181790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:05.077864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:08.810744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:12.348588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:15.945254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:19.596286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:23.216595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:27.042040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:30.718577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:34.331143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:38.112975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:41.931224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:45.597383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:49.517503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:53.212013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:56.882031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:00.570224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:04.432990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:09.426160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:05.309253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:08.999536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:12.542072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:16.168237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:19.780987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:23.426227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:27.233486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:30.918664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:34.525844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:38.317406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:42.134582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:45.816201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:49.717203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:53.406752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:57.076483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:00.770651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:04.660348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:09.727330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:05.555569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:09.205563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:12.745228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:16.377662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:19.994710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:23.646614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:27.446592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:31.139692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:34.736706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:38.536711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:42.351404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:46.067404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:49.934914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:53.618186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:57.280935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:00.998360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:04.953593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:10.014066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:05.791939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:09.400490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:12.939794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:16.585331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:20.193301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:23.860043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:27.647822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:31.345136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:34.950263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:38.860089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:42.556921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:46.295791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:50.140860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:53.822092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:57.478551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:01.213879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:05.231702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:10.270380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:06.029331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:09.687462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:13.126155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:16.772879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:20.384946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:24.067999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:27.832560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:31.547045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:35.166221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:39.062953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:42.750222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:46.485250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:50.346332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:54.016603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:57.658557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:01.439156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:05.473994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:10.556615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:06.282379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:09.878776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:13.330668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:16.992549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:20.591212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:24.289787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:28.046055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:31.761903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:35.400596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:39.280196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:42.961712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:46.697311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:50.565307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:54.230431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:57.860411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:01.683503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:05.767824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:10.906059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:06.499650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:10.072264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:13.532207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:17.188572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:20.784814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:24.499793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:28.249047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:31.971310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:35.626729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:39.490826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:43.168474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:46.904520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:50.776115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:54.429765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:58.055016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:01.904964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:06.082984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:11.116507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:06.682745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:10.252213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:13.710879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:17.366075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:20.964374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:24.692477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:28.433707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:32.153770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:35.836143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:39.675929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:43.352093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:47.089481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:50.965243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:54.612723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:58.224195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:02.096917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:06.323341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:11.459553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:06.899233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:10.465537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:13.915881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:17.574640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:21.173728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:24.913108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:28.643780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:32.376886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:36.077497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:39.931070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:43.567656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:47.297144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:51.186638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:54.831208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:58.430876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:02.315214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:06.622539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:11.750484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:07.104017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:10.650898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:14.102917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:17.761276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:21.362356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:25.143487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:28.949216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:32.576274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:36.284983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:40.131534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:43.763941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:47.490836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:51.387972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:55.036641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:13:58.614022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:02.565558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T09:14:06.884266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-11-26T09:14:25.986895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-26T09:14:26.643059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-26T09:14:27.232716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-26T09:14:27.813933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-26T09:14:28.333566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-26T09:14:28.829269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-26T09:14:12.324955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-26T09:14:13.454823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

X_patient_idX_meshVolumeX_voxelVolumeX_surfaceAreaX_surfaceVolumeRatioX_sphericityX_compactness1X_compactness2X_sphericalDisproportionX_maximum3DDiameterX_maximum2DDiameterSliceX_maximum2DDiameterColumnX_maximum2DDiameterRowX_majorAxisLengthX_minorAxisLengthX_leastAxisLengthX_elongationX_flatnessY_Functional_adenomaY_MicroadenomaY_BleedingY_Cystic_partY_Chiasmal_compressionY_HypopituitarismY_SignalY_Pituitary_stalk_visible
013694.4583333716.01353.8736600.3664610.8536370.0418420.6220421.17145824.51530123.34523523.85372122.36068020.82409118.98469613.6969830.9116700.657747Nonfunctioning adenomaMacroadenomaTrueTrueFalseFalseMixedFalse
127684.3333337709.02103.1954910.2736990.8953830.0449480.7178371.11684129.68164425.80697629.15475927.29468824.62179921.38846020.2577720.8686800.822758Nonfunctioning adenomaMacroadenomaTrueTrueTrueTrueHyposignalTrue
231246.8750001265.0768.5426150.6163750.7289510.0330180.3873421.37183524.69817824.51530124.51530114.21267026.68086710.6535918.0801250.3992970.302843Functional adenomaMicroadenomaFalseTrueFalseFalseHyposignalTrue
3410453.37500010483.02685.1134130.2568660.8610450.0423870.6383771.16138035.21363428.60069934.71311031.40063732.05646922.09619420.8451180.6892900.650262Nonfunctioning adenomaMacroadenomaFalseFalseTrueFalseHypersignalFalse
45780.083333793.0503.7666310.6457860.8134830.0389240.5383271.22928116.12451516.03122016.12451510.81665414.8696009.7897168.1674100.6583710.549269Functional adenomaMicroadenomaFalseFalseFalseTrueHyposignalTrue
5623308.50000023370.04870.5588160.2089610.8101800.0386880.5317951.23429452.15361944.65422739.62322646.40043143.73276030.48781125.8013130.6971390.589977Nonfunctioning adenomaMacroadenomaFalseFalseTrueTrueHypersignalFalse
673627.3750003670.01357.5975500.3742640.8409590.0409130.5947361.18911923.93741820.88061323.02172922.47220519.28814317.74820115.5345890.9201610.805396Nonfunctioning adenomaMacroadenomaFalseTrueTrueTrueHypersignalFalse
7813172.08333313205.03170.5471670.2407020.8507190.0416270.6156851.17547637.54996730.88689036.49657537.21558831.35064925.23567523.9947320.8049490.765366Nonfunctioning adenomaMacroadenomaFalseFalseTrueTrueHypersignalFalse
891715.0416671731.0772.1953410.4502490.8973050.0450930.7224711.11444818.05547017.20465116.64331715.81138815.18838413.34508311.7808650.8786370.775650Functional adenomaMacroadenomaFalseFalseFalseFalseHyposignalTrue
9101350.3333331365.0678.4826510.5024560.8707750.0431080.6602651.14840216.91153515.81138816.64331714.56022014.89870712.05292910.8744660.8089920.729893Functional adenomaMicroadenomaFalseFalseTrueFalseHyposignalTrue
X_patient_idX_meshVolumeX_voxelVolumeX_surfaceAreaX_surfaceVolumeRatioX_sphericityX_compactness1X_compactness2X_sphericalDisproportionX_maximum3DDiameterX_maximum2DDiameterSliceX_maximum2DDiameterColumnX_maximum2DDiameterRowX_majorAxisLengthX_minorAxisLengthX_leastAxisLengthX_elongationX_flatnessY_Functional_adenomaY_MicroadenomaY_BleedingY_Cystic_partY_Chiasmal_compressionY_HypopituitarismY_SignalY_Pituitary_stalk_visible
12132731.5416672750.01123.7499870.4113980.8409170.0409100.5946471.18917823.85372122.47220520.24845718.78829419.37197217.41068711.6442430.8987570.601087Functional adenomaMacroadenomaFalseTrueFalseFalseMixedTrue
131414112.29166714149.03486.5699810.2470590.8099980.0386750.5314381.23457147.10626327.51363342.75511746.57252440.00486523.60606221.7852840.5900800.544566Nonfunctioning adenomaMacroadenomaFalseTrueTrueFalseHypersignalFalse
14152721.9166672740.01069.5375840.3929350.8814640.0439040.6848791.13447621.40093518.43908921.02379619.31320817.35071915.29579914.5284310.8815660.837339Functional adenomaMacroadenomaFalseFalseTrueFalseHyposignalFalse
15178978.0416679008.02506.3698520.2791670.8334770.0403680.5790021.19979434.52535329.15475933.61547330.67572326.48479523.93960121.0129950.9039000.793398Nonfunctioning adenomaMacroadenomaFalseTrueTrueFalseMixedFalse
161911272.58333311303.02771.4320500.2458560.8772610.0435910.6751291.13991233.86738829.68164432.38826933.54102026.89485925.25958823.1115060.9391980.859328Functional adenomaMacroadenomaFalseFalseTrueFalseHyposignalFalse
17201276.9166671291.0643.9294110.5042850.8839360.0440890.6906571.13130316.64331715.81138816.15549413.34166414.66905011.48549710.7777330.7829750.734726Functional adenomaMicroadenomaFalseFalseFalseFalseHyposignalTrue
18221824.9583331841.0820.5404290.4496210.8801420.0438050.6818021.13618018.70828717.46424918.60107516.76305515.85333513.92008411.8468340.8780540.747277Nonfunctioning adenomaMacroadenomaFalseFalseTrueTrueHyposignalFalse
19232710.6666672741.01069.5928210.3945870.8789880.0437190.6791241.13767221.44761120.61552820.59126017.49285618.84529615.37451913.2552830.8158280.703374Functional adenomaMacroadenomaFalseFalseFalseFalseHyposignalTrue
20249780.7083339811.02538.9150800.2595840.8711300.0431340.6610721.14793530.69201930.00000029.83286829.42787826.12091124.68282721.3702800.9449450.818129Nonfunctioning adenomaMacroadenomaFalseTrueTrueTrueHypersignalFalse
21382725.7083332744.01048.2350240.3845730.9002130.0453120.7295171.11084919.28730218.86796218.78829418.38477616.31860115.61578914.9066030.9569320.913473Nonfunctioning adenomaMacroadenomaFalseFalseFalseFalseHyposignalTrue